# Performance Optimization Module for FLEXINVERT.jl
# Advanced performance optimization for large-scale applications

module Performance

using LinearAlgebra
using SparseArrays
using BlockArrays
using Distributed
using SharedArrays
using Base.Threads
using BenchmarkTools
using Profile
using TimerOutputs
using ..CoreTypes: Domain, State, Observations
using ..Covariance: CovarianceMatrix

export PerformanceConfig, optimize_memory, optimize_compute, profile_performance,
       MemoryPool, ComputationCache, ParallelExecutor, GPUAccelerator

"""
    PerformanceConfig

Configuration for performance optimization.

# Fields
- `memory_pool_size::Int`: Size of memory pool in GB
- `use_block_matrices::Bool`: Whether to use block matrix operations
- `parallel_threads::Int`: Number of parallel threads to use
- `cache_size::Int`: Size of computation cache in MB
- `use_sparse_matrices::Bool`: Whether to use sparse matrix representations
- `gpu_acceleration::Bool`: Whether to enable GPU acceleration
- `memory_efficient::Bool`: Whether to use memory-efficient algorithms
- `profiling_enabled::Bool`: Whether to enable performance profiling
"""
Base.@kwdef mutable struct PerformanceConfig
    # Memory configuration
    memory_pool_size::Int = 4  # GB
    use_block_matrices::Bool = true
    cache_size::Int = 512       # MB
    memory_efficient::Bool = true

    # Parallelization
    parallel_threads::Int = Threads.nthreads()
    use_distributed::Bool = false
    chunk_size::Int = 1000

    # Algorithm selection
    use_sparse_matrices::Bool = true
    sparse_threshold::Float64 = 0.1
    use_block_operations::Bool = true
    block_size::Int = 1000

    # GPU acceleration
    gpu_acceleration::Bool = false
    gpu_memory_fraction::Float64 = 0.8

    # Profiling
    profiling_enabled::Bool = true
    detailed_timing::Bool = false

    # Optimization level
    optimization_level::Int = 2  # 0=none, 1=basic, 2=aggressive, 3=maximum
end

"""
    MemoryPool

Memory pool for efficient memory allocation and deallocation.
"""
mutable struct MemoryPool
    pools::Dict{Type, Vector{Vector}}}
    allocated_sizes::Dict{Type, Int}
    total_allocated::Int
    max_memory::Int
    cache_hits::Int
    cache_misses::Int

    function MemoryPool(max_memory::Int = 4 * 1024^3)  # 4GB default
        return new(
            Dict{Type, Vector{Vector}}(),
            Dict{Type, Int}(),
            0,
            max_memory,
            0,
            0
        )
    end
end

"""
    allocate!(pool::MemoryPool, T::Type, size::Int)

Allocate memory from pool with caching.
"""
function allocate!(pool::MemoryPool, T::Type, size::Int)
    # Check if we have a cached array of this size
    if haskey(pool.pools, T)
        cached_arrays = pool.pools[T]
        for (i, arr) in enumerate(cached_arrays)
            if length(arr) >= size
                # Remove from cache and reuse
                deleteat!(cached_arrays, i)
                pool.cache_hits += 1
                return resize!(arr, size)
            end
        end
    end

    # No suitable cached array found, allocate new one
    if pool.total_allocated + size * sizeof(T) > pool.max_memory
        # Free some memory
        cleanup_memory_pool!(pool)
    end

    arr = Vector{T}(undef, size)
    pool.total_allocated += size * sizeof(T)
    pool.cache_misses += 1

    return arr
end

"""
    deallocate!(pool::MemoryPool, arr::Vector{T}) where T

Return array to memory pool cache.
"""
function deallocate!(pool::MemoryPool, arr::Vector{T}) where T
    if !haskey(pool.pools, T)
        pool.pools[T] = Vector{Vector{T}}()
    end

    # Add to cache if not too large
    if length(arr) <= 10000  # Limit cache size
        push!(pool.pools[T], arr)
    else
        pool.total_allocated -= length(arr) * sizeof(T)
    end
end

"""
    cleanup_memory_pool!(pool::MemoryPool)

Clean up memory pool to free memory.
"""
function cleanup_memory_pool!(pool::MemoryPool)
    total_freed = 0

    for (T, arrays) in pool.pools
        for arr in arrays
            total_freed += length(arr) * sizeof(T)
        end
        empty!(arrays)
    end

    pool.total_allocated -= total_freed
end

"""
    ComputationCache

Cache for expensive computations.
"""
mutable struct ComputationCache
    cache::Dict{UInt64, Any}
    max_size::Int
    current_size::Int
    access_times::Dict{UInt64, Float64}
    hits::Int
    misses::Int

    function ComputationCache(max_size::Int = 100)
        return new(
            Dict{UInt64, Any}(),
            max_size,
            0,
            Dict{UInt64, Float64}(),
            0,
            0
        )
    end
end

"""
    get_cached!(cache::ComputationCache, key::UInt64, compute_fn::Function)

Get value from cache or compute and cache it.
"""
function get_cached!(cache::ComputationCache, key::UInt64, compute_fn::Function)
    current_time = time()

    if haskey(cache.cache, key)
        cache.access_times[key] = current_time
        cache.hits += 1
        return cache.cache[key]
    end

    # Compute value
    value = compute_fn()

    # Add to cache
    if cache.current_size >= cache.max_size
        # Remove least recently used item
        lru_key = reduce((k1, k2) -> cache.access_times[k1] < cache.access_times[k2] ? k1 : k2,
                        keys(cache.access_times))
        delete!(cache.cache, lru_key)
        delete!(cache.access_times, lru_key)
        cache.current_size -= 1
    end

    cache.cache[key] = value
    cache.access_times[key] = current_time
    cache.current_size += 1
    cache.misses += 1

    return value
end

"""
    ParallelExecutor

Executor for parallel computations with load balancing.
"""
struct ParallelExecutor
    n_threads::Int
    chunk_size::Int
    load_balancer::Function

    function ParallelExecutor(n_threads::Int = Threads.nthreads(), chunk_size::Int = 1000)
        load_balancer = create_load_balancer(n_threads)
        new(n_threads, chunk_size, load_balancer)
    end
end

"""
    create_load_balancer(n_threads::Int)

Create load balancing function for parallel execution.
"""
function create_load_balancer(n_threads::Int)
    function balance_work(n_items::Int)
        base_chunk = n_items ÷ n_threads
        remainder = n_items % n_threads

        chunks = Vector{UnitRange{Int}}()
        start_idx = 1

        for i in 1:n_threads
            chunk_size = base_chunk + (i <= remainder ? 1 : 0)
            if chunk_size > 0
                end_idx = start_idx + chunk_size - 1
                push!(chunks, start_idx:end_idx)
                start_idx = end_idx + 1
            end
        end

        return chunks
    end

    return balance_work
end

"""
    parallel_map(executor::ParallelExecutor, f::Function, data)

Parallel map with load balancing.
"""
function parallel_map(executor::ParallelExecutor, f::Function, data)
    n_items = length(data)
    chunks = executor.load_balancer(n_items)

    if executor.n_threads == 1 || n_items < executor.chunk_size
        return map(f, data)
    end

    results = Vector{Any}(undef, length(chunks))

    Threads.@threads for i in 1:length(chunks)
        chunk = chunks[i]
        results[i] = map(f, view(data, chunk))
    end

    # Flatten results
    return vcat(results...)
end

"""
    optimize_matrix_operations(A::AbstractMatrix, config::PerformanceConfig)

Optimize matrix operations based on configuration.
"""
function optimize_matrix_operations(A::AbstractMatrix, config::PerformanceConfig)
    if config.use_sparse_matrices && issparse(A)
        return A
    elseif config.use_sparse_matrices && sparsity_ratio(A) < config.sparse_threshold
        return sparse(A)
    elseif config.use_block_matrices && size(A, 1) > config.block_size
        return BlockMatrix(A, config.block_size)
    else
        return A
    end
end

"""
    sparsity_ratio(A::AbstractMatrix)

Calculate sparsity ratio of matrix.
"""
function sparsity_ratio(A::AbstractMatrix)
    n_elements = length(A)
    n_zeros = count(x -> x == 0, A)
    return n_zeros / n_elements
end

"""
    BlockMatrix

Block matrix representation for efficient operations.
"""
struct BlockMatrix
    blocks::Vector{Matrix{Float64}}
    block_size::Int
    n_blocks_row::Int
    n_blocks_col::Int
    original_size::Tuple{Int, Int}
end

function BlockMatrix(A::AbstractMatrix, block_size::Int)
    m, n = size(A)
    n_blocks_row = cld(m, block_size)
    n_blocks_col = cld(n, block_size)

    blocks = Vector{Matrix{Float64}}()

    for i in 1:n_blocks_row
        for j in 1:n_blocks_col
            row_start = (i-1) * block_size + 1
            row_end = min(i * block_size, m)
            col_start = (j-1) * block_size + 1
            col_end = min(j * block_size, n)

            block = A[row_start:row_end, col_start:col_end]
            push!(blocks, block)
        end
    end

    return BlockMatrix(blocks, block_size, n_blocks_col, n_blocks_row, (m, n))
end

"""
    multiply_optimized(A::BlockMatrix, x::AbstractVector)

Optimized matrix-vector multiplication for block matrices.
"""
function multiply_optimized(A::BlockMatrix, x::AbstractVector)
    m, n = A.original_size
    result = zeros(Float64, m)

    for block_i in 1:A.n_blocks_row
        for block_j in 1:A.n_blocks_col
            block_idx = (block_i - 1) * A.n_blocks_col + block_j
            block = A.blocks[block_idx]

            row_start = (block_i - 1) * A.block_size + 1
            row_end = min(block_i * A.block_size, m)
            col_start = (block_j - 1) * A.block_size + 1
            col_end = min(block_j * A.block_size, n)

            result[row_start:row_end] += block * x[col_start:col_end]
        end
    end

    return result
end

"""
    optimize_memory(config::PerformanceConfig)

Optimize memory usage for FLEXINVERT operations.
"""
function optimize_memory(config::PerformanceConfig)
    @info "Optimizing memory usage..."

    # Create memory pool
    memory_pool = MemoryPool(config.memory_pool_size * 1024^3)

    # Create computation cache
    computation_cache = ComputationCache(config.cache_size * 1024^2)

    # Enable memory-efficient algorithms if requested
    if config.memory_efficient
        @info "Enabling memory-efficient algorithms"
        # Set up in-place operations where possible
    end

    return memory_pool, computation_cache
end

"""
    optimize_compute(config::PerformanceConfig)

Optimize computation performance.
"""
function optimize_compute(config::PerformanceConfig)
    @info "Optimizing computation performance..."

    # Set up parallel execution
    parallel_executor = ParallelExecutor(config.parallel_threads)

    # Optimize BLAS settings
    BLAS.set_num_threads(min(config.parallel_threads, 4))

    # Enable GPU acceleration if requested
    gpu_accelerator = nothing
    if config.gpu_acceleration
        @info "Enabling GPU acceleration"
        gpu_accelerator = setup_gpu_acceleration(config)
    end

    return parallel_executor, gpu_accelerator
end

"""
    setup_gpu_acceleration(config::PerformanceConfig)

Set up GPU acceleration (placeholder implementation).
"""
function setup_gpu_acceleration(config::PerformanceConfig)
    # This would integrate with CUDA.jl or other GPU packages
    @warn "GPU acceleration not yet implemented"
    return nothing
end

"""
    profile_performance(f::Function, config::PerformanceConfig)

Profile function performance with detailed timing.
"""
function profile_performance(f::Function, config::PerformanceConfig)
    if !config.profiling_enabled
        return f()
    end

    @info "Profiling performance..."

    # Set up timer
    timer = TimerOutput()

    # Profile the function
    result = @timeit timer "function_call" begin
        if config.detailed_timing
            # More detailed profiling
            Profile.clear()
            Profile.init()
            result = @profile f()
        else
            result = f()
        end
    end

    # Print timing results
    if config.detailed_timing
        println(timer)
        Profile.print()
    end

    return result, timer
end

"""
    benchmark_operations(domain::Domain, observations::Observations, config::PerformanceConfig)

Benchmark key operations to identify bottlenecks.
"""
function benchmark_operations(domain::Domain, observations::Observations, config::PerformanceConfig)
    @info "Benchmarking key operations..."

    results = Dict{String, Any}()

    # Benchmark covariance matrix construction
    if config.optimization_level >= 1
        cov_time = @benchmark begin
            # Placeholder covariance construction
            A = rand(1000, 1000)
            A = A' * A  # Make positive definite
        end samples=10 evals=1
        results[:covariance_construction] = cov_time
    end

    # Benchmark matrix operations
    if config.optimization_level >= 2
        # Matrix multiplication
        A = rand(1000, 1000)
        B = rand(1000, 1000)
        matmul_time = @benchmark A * B samples=10 evals=1
        results[:matrix_multiplication] = matmul_time

        # Linear solve
        x = rand(1000)
        solve_time = @benchmark A \ x samples=10 evals=1
        results[:linear_solve] = solve_time
    end

    # Benchmark forward model
    forward_time = @benchmark begin
        # Placeholder forward model
        concentrations = rand(length(observations.concentrations))
    end samples=10 evals=1
    results[:forward_model] = forward_time

    return results
end

"""
    auto_tune!(config::PerformanceConfig, domain::Domain, observations::Observations)

Automatically tune performance parameters based on system characteristics.
"""
function auto_tune!(config::PerformanceConfig, domain::Domain, observations::Observations)
    @info "Auto-tuning performance parameters..."

    # Determine optimal thread count
    available_threads = Threads.nthreads()
    problem_size = domain.nx * domain.ny * length(observations.concentrations)

    if problem_size < 10000
        config.parallel_threads = 1
    elseif problem_size < 100000
        config.parallel_threads = min(available_threads, 2)
    else
        config.parallel_threads = available_threads
    end

    # Determine optimal block size
    if config.use_block_matrices
        memory_per_block = 1000^2 * sizeof(Float64)  # ~8MB per 1000x1000 block
        available_memory = config.memory_pool_size * 1024^3
        max_blocks = available_memory ÷ memory_per_block

        # Choose block size to fit in memory
        config.block_size = min(1000, Int(sqrt(max_blocks)) * 100)
    end

    # Determine if sparse matrices are beneficial
    if config.use_sparse_matrices
        # This would be based on actual sparsity analysis
        config.sparse_threshold = 0.1
    end

    # Configure GPU acceleration based on problem size
    if config.gpu_acceleration
        config.gpu_acceleration = problem_size > 100000
    end

    @info "Auto-tuning completed:"
    @info "  Parallel threads: $(config.parallel_threads)"
    @info "  Block size: $(config.block_size)"
    @info "  GPU acceleration: $(config.gpu_acceleration)"
end

"""
    optimize_inversion_workflow(state::State, observations::Observations, domain::Domain,
                              covariance::CovarianceMatrix, config::PerformanceConfig)

Optimize the complete inversion workflow.
"""
function optimize_inversion_workflow(state::State, observations::Observations, domain::Domain,
                                   covariance::CovarianceMatrix, config::PerformanceConfig)

    # Auto-tune if needed
    if config.optimization_level > 0
        auto_tune!(config, domain, observations)
    end

    # Set up performance optimizations
    memory_pool, computation_cache = optimize_memory(config)
    parallel_executor, gpu_accelerator = optimize_compute(config)

    @info "Optimized inversion workflow ready"
    @info "  Memory pool: $(config.memory_pool_size) GB"
    @info "  Parallel threads: $(config.parallel_threads)"
    @info "  Block matrices: $(config.use_block_matrices)"
    @info "  Sparse matrices: $(config.use_sparse_matrices)"

    return (
        memory_pool = memory_pool,
        computation_cache = computation_cache,
        parallel_executor = parallel_executor,
        gpu_accelerator = gpu_accelerator
    )
end

"""
    monitor_performance(optimizations::NamedTuple)

Monitor performance during execution.
"""
function monitor_performance(optimizations::NamedTuple)
    memory_pool = optimizations.memory_pool
    computation_cache = optimizations.computation_cache

    @info "Performance Monitor:"
    @info "  Memory pool usage: $(round(memory_pool.total_allocated / 1024^3, digits=2)) GB"
    @info "  Cache hits: $(memory_pool.cache_hits)"
    @info "  Cache misses: $(memory_pool.cache_misses)"
    @info "  Cache hit rate: $(round(memory_pool.cache_hits / (memory_pool.cache_hits + memory_pool.cache_misses) * 100, digits=1))%"
    @info "  Computation cache: $(computation_cache.current_size)/$(computation_cache.max_size) items"
    @info "  Computation cache hit rate: $(round(computation_cache.hits / (computation_cache.hits + computation_cache.misses) * 100, digits=1))%"
end

end # module